在大反演范围内, 小波正则化反演(WRIM)是一种改进动态光散射(DLS)数据反演精度的有效方法, 但该方法对强噪声及双峰颗粒数据的反演精度偏低。在WRIM基础上, 结合传统的单尺度Tikhonov、 截断奇异值分解(TSVD)正则化在动态光散射反演中的优点, 提出了一种Tikhonov-TSVD-WRIM(TTWRIM)多尺度动态光散射反演方法。该方法首先将Tikhonov用于粗尺度反演范围的自适应调整, 然后将TSVD用于细尺度反演, 并对反演结果进行五点三次平滑处理。在0.001、0.005及0.01三种噪声下, 分别采用Tikhonov、TSVD、WRIM及TTWRIM四种方法对模拟数据进行了反演, 结果表明, TTWRIM反演精度更高、抗干扰能力及双峰分辨力更强。最后, 实测颗粒的反演也验证了模拟数据的结论。在［1 nm,2000 nm］的反演范围内, TTWRIM对300 nm单峰及100~500 nm双峰实测颗粒的反演峰值误差分别为0.18％和2.81％。

In the large inversion range, wavelet-regularization inversion method (WRIM) is an effective method to improve the inversion accuracy of dynamic light scattering (DLS) data. However, the inversion accuracy of WRIM is still low for the strong noise and bimodal particles data. Based on WRIM, combined with the advantages of the traditional single scale Tikhonov and Truncated singular value decomposition (TSVD) regularization in DLS inversion, the multi-scale Tikhonov-TSVD-WRIM (TTWRIM) used for the DLS data is proposed. In this method, Tikhonov is applied to the adaptive adjustment of the coarse scale inversion range, and then TSVD is applied to the fine scale inversion, and its inversion result is processed by cubical smoothing algorithm with five-point approximation. Under three kinds of noise levels with 0.001, 0.005 and 0.01, the simulated data are inverted by Tikhonov, TSVD, WRIM and TTWRIM, respectively. The results show that TTWRIM has a high accuracy, strong anti-interference ability and bimodal resolution. Finally, the inversions of the measured particles also verify the conclusions of the simulated data. In the inversion range of ［1 nm, 2000 nm］, the peak value error of TTWRIM for 300 nm unimodal and 100~500 nm bimodal measured particles are 0.18% and 2.81%, respectively.